We validate the effectiveness of our proposed approach on two challenging synthetic-to-real SSDA semantic segmentation tasks. We use commonly used synthetic datasets (GTA5 [1]} and SYNTHIA [2]}) for the source domain, and a real-world image dataset (Cityscapes [3]}) for the target domain. The split into training and testing sets is done in a standard way as defined by other work (e.g. Chen et al. [4]}).
We train a model using a large amount of labeled source data, a small set of labeled target data, and the remaining unlabeled target data.
